These results almost certainly reflect a type II error from a lack of power. Extended follow-up increased power. Statistical measures were not adjusted for multiple comparisons; such adjustments would have further reduced.

Statistical anomalies hide profound weakness. One economist suggests that regardless of the source of his income, there are only two things he can do… Spend it or invest it and we know how to measure consumption and investment.

Type I and type II errors are part of the process of hypothesis testing. What is the difference between these types of errors?

web service – In this post, I’ll walk through building a web service with F# and.NET Core 2.0 using the Giraffe library. This contains the web API routes that we’ll be adding to.

What is a 'Type II Error' A type II error is a statistical term used within the context of hypothesis testing that describes the error that occurs when one accepts a.